People spend an ever-increasing amount of time interacting with computers, and consume a vast amount of computer-delivered media. This interaction can be for many different reasons, such as a desire to find educational or entertaining content, to interact with others using social media, to create documents, and to play games, to name a few examples.
In some cases, the human-computer interaction can take the form of a person performing a task using a software-based tool running on a computer. Examples include creating a document, editing a video, and/or doing one or more of the numerous other activities a modern computer can perform. The person might find the execution of certain activities interesting or even exciting, and might be surprised at how easy it is to perform the activity. The person can become excited, happy, or content as he or she performs an interesting or exciting activity. On the other hand, the person might find some activities difficult to perform, and might become frustrated or even angry with the computer or software tool. In some cases, for example, users are surveyed in an attempt to determine whether or not a computer or computer program functioned well and to identify where the computer program might need improvement. However, such survey results are often unreliable because the surveys are often completed well after the activity was performed. In addition, survey participation rates can be low, and people may not provide accurate and honest answers to the survey.
In other cases of human-computer interaction, a person might not be using a software tool to accomplish a task, but instead might be consuming computer-accessed content or media such as news, pictures, music, or video. Currently, people consuming computer-driven content can tediously self-rate the media if they wish to communicate personal preferences. In some cases, viewers enter a specific number of stars corresponding to a level of like or dislike, while in other cases, users are asked to answer a list of questions. While a system for collecting users' evaluations of media and other products or services can be a helpful metric, current evaluation schemes are often tedious and challenging. Recommendations based on such a system of star rating and/or other means of self-reporting are imprecise, subjective, unreliable, and are further limited by sample size, as only a small number of viewers prove to actually rate the media they consume. Thus, in many cases, such subjective evaluation is neither a reliable nor practical way to evaluate personal responses to media.
A third-party observer can also be used to evaluate the human-computer interaction. A trained observer can often infer the user's mental state simply by observing the individual, their actions, and their context—e.g. their environment. The third party might also interact with the user and ask them questions about how they are feeling or details about what they are doing. While such a methodology can provide interesting results, the need for a trained observer to view and analyze the user means that using third-party observers is not scalable to large numbers of people performing many tasks in many locations. It also might be possible that the mere presence of the observer impacts the user's mental state, generating questionable results.